Abstract
This paper is about state estimation in a timed probabilistic setting. The main contribution is a general procedure to design an observer for computing the probabilities of the states for labeled continuous time Markov models as functions of time, based on a sequence of observations and their associated time stamps that have been collected thus far. Two notions of state consistency with respect to such a timed observation sequence are introduced and related necessary and sufficient conditions are derived. The method is then applied to the detection of cyber-attacks. The plant and the possible attacks are described in terms of a labeled continuous time Markov model that includes both observable and unobservable events, and where each attack corresponds to a particular subset of states. Consequently, attack detection is reformulated as a state estimation problem.
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20 June 2022
A Correction to this paper has been published: https://doi.org/10.1007/s10626-022-00364-6
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Lefebvre, D., Seatzu, C., Hadjicostis, C.N. et al. Probabilistic state estimation for labeled continuous time Markov models with applications to attack detection. Discrete Event Dyn Syst 32, 65–88 (2022). https://doi.org/10.1007/s10626-021-00348-y
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DOI: https://doi.org/10.1007/s10626-021-00348-y